Prediction of Earnings per Share for Industry

Swati Jadhav, Hongmei He, Karl Jenkins

2015

Abstract

Prediction of Earnings Per Share (EPS) is the fundamental problem in finance industry. Various Data Mining technologies have been widely used in computational finance. This research work aims to predict the future EPS with previous values through the use of data mining technologies, thus to provide decision makers a reference or evidence for their economic strategies and business activity. We created three models LR, RBF and MLP for the regression problem. Our experiments with these models were carried out on the real datasets provided by a software company. The performance assessment was based on Correlation Coefficient and Root Mean Squared Error. These algorithms were validated with the data of six different companies. Some differences between the models have been observed. In most cases, Linear Regression and Multilayer Perceptron are effectively capable of predicting the future EPS. But for the high nonlinear data, MLP gives better performance.

References

  1. Arefin, J. & Rahman, R.M. 2011, "Testing different forms of efficiency for Dhaka Stock Exchange", International Journal of Financial Services Management, vol. 5, no. 1, pp. 1-20.
  2. Bagheri, A., Peyhani, H.M. & Akbari, M. 2014, "Financial forecasting using ANFIS networks with quantumbehaved particle swarm optimization", Expert Systems with Applications, vol. 41, no. 14, pp. 6235-6250.
  3. Blair, B.J., Poon, S. & Taylor, S.J. 2010, "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns" in Handbook of Quantitative Finance and Risk Management Springer, , pp. 1333-1344.
  4. Boyacioglu, M.A. & Avci, D. 2010, "An adaptive networkbased fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange", Expert Systems with Applications, vol. 37, no. 12, pp. 7908-7912.
  5. Chen, K., Lin, H. & Huang, T. 2009, "The prediction of Taiwan 10-year government bond yield", WSEAS Transactions on Systems, vol. 8, no. 9, pp. 1051-1060.
  6. Chen, M. 2013, "A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering", Information Sciences, vol. 220, pp. 180-195.
  7. Chen, W. & Du, Y. 2009, "Using neural networks and data mining techniques for the financial distress prediction model", Expert Systems with Applications, vol. 36, no. 2, pp. 4075-4086.
  8. De Oliveira, F.A., Zárate, L.E., de Azevedo Reis, M. & Nobre, C.N. 2011, "The use of artificial neural networks in the analysis and prediction of stock prices", Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference onIEEE, , pp. 2151.
  9. Du Jardin, P. & Séverin, E. 2011, "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model", Decision Support Systems, vol. 51, no. 3, pp. 701-711.
  10. Esichaikul, V. & Srithongnopawong, P. 2010, "Using relative movement to support ANN-based stock forecasting in Thai stock market", International Journal of Electronic Finance, vol. 4, no. 1, pp. 84-98.
  11. Fayyad, U., Piatetsky-Shapiro, G. & Smyth, P. 1996, "From data mining to knowledge discovery in databases", AI magazine, vol. 17, no. 3, pp. 37.
  12. Geng, R., Bose, I. & Chen, X. 2015, "Prediction of financial distress: An empirical study of listed Chinese companies using data mining", European Journal of Operational Research, vol. 241, no. 1, pp. 236-247.
  13. Guo, Z., Wang, H., Yang, J. & Miller, D.J. 2015, "A Stock Market Forecasting Model Combining TwoDirectional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network", .
  14. Guresen, E., Kayakutlu, G. & Daim, T.U. 2011, "Using artificial neural network models in stock market index prediction", Expert Systems with Applications, vol. 38, no. 8, pp. 10389-10397.
  15. Han, S. & Chen, R. 2007, "Using svm with financial statement analysis for prediction of stocks", Communications of the IIMA, vol. 7, no. 4, pp. 63.
  16. Hsieh, N. & Hung, L. 2010, "A data driven ensemble classifier for credit scoring analysis", Expert Systems with Applications, vol. 37, no. 1, pp. 534-545.
  17. Ince, H. & Trafalis, T.B. 2008, "Short term forecasting with support vector machines and application to stock price prediction", International Journal of General Systems, vol. 37, no. 6, pp. 677-687.
  18. Jiang, Y., Wang, H. & Xie, Q. 2009, "Classification model of companies' financial performance based on integrated support vector machine", Management Science and Engineering, 2009. ICMSE 2009. International Conference on IEEE, , pp. 1322.
  19. Kara, Y., Boyacioglu, M.A. & Baykan, ÖK. 2011, "Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange", Expert Systems with Applications, vol. 38, no. 5, pp. 5311-5319.
  20. Kazem, A., Sharifi, E., Hussain, F.K., Saberi, M. & Hussain, O.K. 2013, "Support vector regression with chaos-based firefly algorithm for stock market price forecasting", Applied Soft Computing, vol. 13, no. 2, pp. 947-958.
  21. Khirbat, G., Gupta, R. & Singh, S. 2013, "Optimal Neural Network Architecture for Stock Market Forecasting", Communication Systems and Network Technologies (CSNT), 2013 International Conference onIEEE, , pp. 557.
  22. Lai, R.K., Fan, C., Huang, W. & Chang, P. 2009, "Evolving and clustering fuzzy decision tree for financial time series data forecasting", Expert Systems with Applications, vol. 36, no. 2, pp. 3761-3773.
  23. Li, H. & Wong, M. 2014, "Knowledge discovering in corporate securities fraud by using grammar based genetic programming", Journal of Computer and Communications, vol. 2, no. 04, pp. 148.
  24. Mostafa, M.M. 2010, "Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait", Expert Systems with Applications, vol. 37, no. 9, pp. 6302-6309.
  25. Ögüt, H., Doganay, M.M., Ceylan, N.B. & Aktas, R. 2012, "Prediction of bank financial strength ratings: The case of Turkey", Economic Modelling, vol. 29, no. 3, pp. 632-640.
  26. Olaniyi, S.A.S., Adewole, K.S. & Jimoh, R. 2011, "Stock trend prediction using regression analysis-a data mining approach", ARPN Journal of Systems and Software, vol. 1, no. 4, pp. 154-157.
  27. Pacelli, V., Bevilacqua, V. & Azzollini, M. 2011, "An artificial neural network model to forecast exchange rates", Journal of Intelligent Learning Systems and Applications, vol. 3, no. 02, pp. 57.
  28. Pan, N.H., Lee, M.L. & Chang, C.W. 2011, "Construction Financial Crisis Warning Model Using Data Mining", Advanced Materials Research Trans Tech Publ, , pp. 684.
  29. Pan, W. 2012, "A new fruit fly optimization algorithm: taking the financial distress model as an example", Knowledge-Based Systems, vol. 26, pp. 69-74.
  30. Patell, J.M. 1976, "Corporate forecasts of earnings per share and stock price behavior: Empirical test", Journal of accounting research, , pp. 246-276.
  31. Qiu, X.Y. 2007, "On building predictive models with company annual reports", .
  32. Quah, J.T. & Ng, W. 2007, "Utilizing computational intelligence for DJIA stock selection", Neural Networks, 2007. IJCNN 2007. International Joint Conference onIEEE, , pp. 956.
  33. Quah, T. 2008, "DJIA stock selection assisted by neural network", Expert Systems with Applications, vol. 35, no. 1, pp. 50-58.
  34. Rajakumar, M.P. & Shanthi, V. 2014, "Forecasting earnings per share for companies in it sector using Markov process model", Journal of Theoretical and Applied Information Technology, vol. 59, no. 2, pp. 332-341.
  35. Ravisankar, P. & Ravi, V. 2010, "Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP", Knowledge-Based Systems, vol. 23, no. 8, pp. 823-831.
  36. Ravisankar, P., Ravi, V., Rao, G.R. & Bose, I. 2011, "Detection of financial statement fraud and feature selection using data mining techniques", Decision Support Systems, vol. 50, no. 2, pp. 491-500.
  37. Rezaie, K., Dalfard, V.M., Hatami-Shirkouhi, L. & NazariShirkouhi, S. 2013, "Efficiency appraisal and ranking of decision-making units using data envelopment analysis in fuzzy environment: a case study of Tehran stock exchange", Neural Computing and Applications, vol. 23, no. 1, pp. 1-17.
  38. Saigal, S. & Mehrotra, D. 2012, "Performance comparison of time series data using predictive data mining techniques", Advances in Information Mining, vol. 4, no. 1, pp. 57-66.
  39. Sajja, P.S. & Akerkar, R. 2012, Intelligent technologies for Web applications, CRC Press.
  40. San Ong, T., Yichen, Y.N. & Teh, B.H. 2010, "Can High Price Earnings Ratio Act As An Indicator Of The Coming Bear Market In The Malaysia?", International Journal Of Business And Social Science, vol. 1, no. 1.
  41. Sermpinis, G., Theofilatos, K., Karathanasopoulos, A., Georgopoulos, E.F. & Dunis, C. 2013, "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and particle swarm optimization", European Journal of Operational Research, vol. 225, no. 3, pp. 528-540.
  42. Serrano-Cinca, C. & GutiéRrez-Nieto, B. 2013, "Partial least square discriminant analysis for bankruptcy prediction", Decision Support Systems, vol. 54, no. 3, pp. 1245-1255.
  43. Shen, W., Guo, X., Wu, C. & Wu, D. 2011, "Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm", Knowledge-Based Systems, vol. 24, no. 3, pp. 378-385.
  44. Song, X., Ding, Y., Huang, J. & Ge, Y. 2010, "Feature selection for support vector machine in financial crisis prediction: a case study in China", Expert Systems, vol. 27, no. 4, pp. 299-310.
  45. Timor, M., Dincer, H. & Emir, S. 2012, "Performance comparison of artificial neural network (ANN) and support vector machines (SVM) models for the stock selection problem: An application on the Istanbul Stock Exchange (ISE)-30 index in Turkey", .
  46. Vaisla, K.S. & Bhatt, A.K. 2010, "An analysis of the performance of artificial neural network technique for stock market forecasting", International Journal on Computer Science and Engineering, vol. 2, no. 6, pp. 2104-2109.
  47. Wang, J., Wang, J., Zhang, Z. & Guo, S. 2011, "Forecasting stock indices with back propagation neural network", Expert Systems with Applications, vol. 38, no. 11, pp. 14346-14355.
  48. Wong, C. & Versace, M. 2012, "CARTMAP: a neural network method for automated feature selection in financial time series forecasting", Neural Computing and Applications, vol. 21, no. 5, pp. 969-977.
  49. Yan, X., Wang, Z., Yu, S. & Li, Y. 2005, "Time series forecasting with RBF neural network", Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference onIEEE, , pp. 4680.
Download


Paper Citation


in Harvard Style

Jadhav S., He H. and Jenkins K. (2015). Prediction of Earnings per Share for Industry . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 425-432. DOI: 10.5220/0005616604250432


in Bibtex Style

@conference{kdir15,
author={Swati Jadhav and Hongmei He and Karl Jenkins},
title={Prediction of Earnings per Share for Industry},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={425-432},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005616604250432},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - Prediction of Earnings per Share for Industry
SN - 978-989-758-158-8
AU - Jadhav S.
AU - He H.
AU - Jenkins K.
PY - 2015
SP - 425
EP - 432
DO - 10.5220/0005616604250432